| Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics NH Paulson, MW Priddy, DL McDowell, SR Kalidindi Acta Materialia 129, 428-438, 2017 | 188 | 2017 |
| Feature engineering for machine learning enabled early prediction of battery lifetime NH Paulson, J Kubal, L Ward, S Saxena, W Lu, SJ Babinec Journal of Power Sources 527, 231127, 2022 | 157 | 2022 |
| Correlations between thermal history and keyhole porosity in laser powder bed fusion NH Paulson, B Gould, SJ Wolff, M Stan, AC Greco Additive Manufacturing 34, 101213, 2020 | 126 | 2020 |
| A convolutional neural network model for battery capacity fade curve prediction using early life data S Saxena, L Ward, J Kubal, W Lu, S Babinec, N Paulson Journal of Power Sources 542, 231736, 2022 | 105 | 2022 |
| Principles of the battery data genome L Ward, S Babinec, EJ Dufek, DA Howey, V Viswanathan, M Aykol, ... Joule 6 (10), 2253-2271, 2022 | 103 | 2022 |
| Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures NH Paulson, MW Priddy, DL McDowell, SR Kalidindi Materials & Design 154, 170-183, 2018 | 78 | 2018 |
| Quantified uncertainty in thermodynamic modeling for materials design NH Paulson, BJ Bocklund, RA Otis, ZK Liu, M Stan Acta Materialia 174, 9-15, 2019 | 68 | 2019 |
| Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives A Thelen, X Huan, N Paulson, S Onori, Z Hu, C Hu npj Materials Sustainability 2 (1), 14, 2024 | 63 | 2024 |
| Bayesian strategies for uncertainty quantification of the thermodynamic properties of materials NH Paulson, E Jennings, M Stan International Journal of Engineering Science 142, 74-93, 2019 | 58 | 2019 |
| Strategies for rapid parametric assessment of microstructure-sensitive fatigue for HCP polycrystals MW Priddy, NH Paulson, SR Kalidindi, DL McDowell International Journal of Fatigue 104, 231-242, 2017 | 58 | 2017 |
| Reduced-order microstructure-sensitive protocols to rank-order the transition fatigue resistance of polycrystalline microstructures NH Paulson, MW Priddy, DL McDowell, SR Kalidindi International Journal of Fatigue 119, 1-10, 2019 | 43 | 2019 |
| Computational fluid dynamics modeling and analysis of silica nanoparticle synthesis in a flame spray pyrolysis reactor D Dasgupta, P Pal, R Torelli, S Som, N Paulson, J Libera, M Stan Combustion and Flame 236, 111789, 2022 | 32 | 2022 |
| Thermodynamics of monoclinic and tetragonal hafnium dioxide (HfO2) at ambient pressure JJ Low, NH Paulson, M D'Mello, M Stan Calphad 72, 102210, 2021 | 32 | 2021 |
| Flame spray pyrolysis optimization via statistics and machine learning NH Paulson, JA Libera, M Stan Materials & Design 196, 108972, 2020 | 30 | 2020 |
| Uncertainty quantification and propagation in CALPHAD modeling P Honarmandi, NH Paulson, R Arróyave, M Stan Modelling and Simulation in Materials Science and Engineering 27 (3), 034003, 2019 | 29 | 2019 |
| Flame stability analysis of flame spray pyrolysis by artificial intelligence J Pan, JA Libera, NH Paulson, M Stan The International Journal of Advanced Manufacturing Technology 114 (7), 2215 …, 2021 | 23 | 2021 |
| Intelligent agents for the optimization of atomic layer deposition NH Paulson, A Yanguas-Gil, OY Abuomar, JW Elam ACS Applied Materials & Interfaces 13 (14), 17022-17033, 2021 | 23 | 2021 |
| Comparison of statistically-based methods for automated weighting of experimental data in CALPHAD-type assessment NH Paulson, S Zomorodpoosh, I Roslyakova, M Stan Calphad 68, 101728, 2020 | 23 | 2020 |
| Uncertainty quantification in atomistic modeling of metals and its effect on mesoscale and continuum modeling: A review JJ Gabriel, NH Paulson, TC Duong, F Tavazza, CA Becker, S Chaudhuri, ... Jom 73 (1), 149-163, 2021 | 17 | 2021 |
| Multivariate prognosis of battery advanced state of health via transformers NH Paulson, J Kubal, SJ Babinec Cell Reports Physical Science 5 (5), 2024 | 10 | 2024 |
Marius StanArgonne National LaboratoryVerified email at anl.gov
